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Modern high-throughput screening technologies allow for the testing of tens of thousands of compounds per day. However, a substantial proportion of the initial hits can be artifacts related to aggregate formation [1], chemical reactivity, photoreactivity, redox activity, metal chelation, interference with assay spectroscopy, membrane disruption, decomposition in buffers and other mechanisms [2–4].

The seminal works by the Shoichet group on aggregators [1] and by Baell and Holloway on pan-assay interference compounds (PAINS) [2] have greatly increased the scientific community's awareness of the pollution of medicinal chemistry and chemical biology literature with ‘bad actors’ and ‘frequent hitters’. Less present in discussions but not of lower significance are impurities and decomposition products as sources of assay interference [15,16].

Recently, the editors-in-chief of nine ACS journals have teamed up to define best practice guidelines for how to identify assay artifacts and reject such hits [4]. Recommendations include the measurement and publication of full concentration response curves as well as the use of reporter-free methods such as surface plasmon resonance.

At this point, it is important to note that frequent hitters are not necessarily bad actors and vice versa. Frequent hitters are compounds which have a higher-than-expected activity rate recorded in historical screening data. Bad actors, on the other hand, are compounds that trigger false assay readouts under specific conditions and therefore often, but by far not always, show a high frequency of false readouts. In addition to some bad actors, frequent hitters also include true promiscuous compounds (sometimes related to privileged scaffolds) that may in fact be of interest in the context of polypharmacology and drug repurposing.

Computational methods can make a significant contribution to the identification of potential bad actors and/or frequent hitters. These computational techniques include rule-based and similarity-based methods, statistical approaches and machine learning. Here, we will briefly discuss the most relevant approaches that are publicly accessible.

Future Medicinal Chemistry provides a monthly point of access to commentary and debate for this ever-expanding and diversifying community. The journal showcases milestones in pharmaceutical R&D and features expert analysis of emerging research – from the identification of targets, through to the discovery, design, synthesis and evaluation of bioactive agents.